



'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase_   :					Any        =     logging.getLogger(__name__)




@dataclass
class   __UpperCamelCase							:
	__A      	=       field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
	__A      	=       field(
	    default=__lowerCAmelCase      , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
	__A      	=       field(
	    default='''NER'''      , metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}      )
	__A      	=       field(
	    default=__lowerCAmelCase      , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
	__A      	=       field(default=__lowerCAmelCase      , metadata={'''help''': '''Set this flag to use fast tokenization.'''}      )
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	__A      	=       field(
	    default=__lowerCAmelCase      , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}      , )




@dataclass
class   __UpperCamelCase							:
	__A      	=       field(
	    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}      )
	__A      	=       field(
	    default=__lowerCAmelCase      , metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}      , )
	__A      	=       field(
	    default=128      , metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }      , )
	__A      	=       field(
	    default=__lowerCAmelCase      , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )







def   SCREAMING_SNAKE_CASE					(					):
						lowercase   =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
						if len(sys.argv    ) == 2 and sys.argv[1].endswith(""".json"""    ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowercase  , lowercase  , lowercase   =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
						else:
												lowercase  , lowercase  , lowercase   =				parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir    )
						    and os.listdir(training_args.output_dir    )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    """ --overwrite_output_dir to overcome."""    )

						lowercase   =				import_module("""tasks"""    )
						try:
												lowercase   =				getattr(UpperCamelCase_			,	model_args.task_type    )
												lowercase   =				token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
												    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""    )

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,	datefmt="""%m/%d/%Y %H:%M:%S"""			,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,	)
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""			,	training_args.local_rank			,	training_args.device			,	training_args.n_gpu			,	bool(training_args.local_rank != -1    )			,	training_args.fpaa			,	)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank    ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("""Training/evaluation parameters %s"""			,	UpperCamelCase_    )

						# Set seed
						set_seed(training_args.seed    )

						# Prepare CONLL-2003 task
						lowercase   =				token_classification_task.get_labels(data_args.labels    )
						lowercase   =				dict(enumerate(UpperCamelCase_    )    )
						lowercase   =				len(UpperCamelCase_    )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowercase   =				AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,	num_labels=UpperCamelCase_			,	idalabel=UpperCamelCase_			,	labelaid={label: i for i, label in enumerate(UpperCamelCase_    )}			,	cache_dir=model_args.cache_dir			,	)
						lowercase   =				AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,	cache_dir=model_args.cache_dir			,	use_fast=model_args.use_fast			,	)
						lowercase   =				AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path			,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )			,	config=UpperCamelCase_			,	cache_dir=model_args.cache_dir			,	)

						# Get datasets
						lowercase   =				(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_			,	data_dir=data_args.data_dir			,	tokenizer=UpperCamelCase_			,	labels=UpperCamelCase_			,	model_type=config.model_type			,	max_seq_length=data_args.max_seq_length			,	overwrite_cache=data_args.overwrite_cache			,	mode=Split.train			,	)
						    if training_args.do_train
						    else None
						)
						lowercase   =				(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_			,	data_dir=data_args.data_dir			,	tokenizer=UpperCamelCase_			,	labels=UpperCamelCase_			,	model_type=config.model_type			,	max_seq_length=data_args.max_seq_length			,	overwrite_cache=data_args.overwrite_cache			,	mode=Split.dev			,	)
						    if training_args.do_eval
						    else None
						)

						def align_predictions(lowercase_     :   Optional[int]			,	lowercase_     :   int    ) -> Tuple[List[int], List[int]]:
												lowercase   =				np.argmax(UpperCamelCase_			,	axis=2    )

												lowercase  , lowercase   =				preds.shape

												lowercase   =				[[] for _ in range(UpperCamelCase_    )]
												lowercase   =				[[] for _ in range(UpperCamelCase_    )]

												for i in range(UpperCamelCase_    ):
																		for j in range(UpperCamelCase_    ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]    )
																														preds_list[i].append(label_map[preds[i][j]]    )

												return preds_list, out_label_list

						def compute_metrics(lowercase_     :   Dict    ) -> Dict:
												lowercase  , lowercase   =				align_predictions(p.predictions			,	p.label_ids    )
												return {
												    "accuracy_score": accuracy_score(UpperCamelCase_			,	UpperCamelCase_    ),
												    "precision": precision_score(UpperCamelCase_			,	UpperCamelCase_    ),
												    "recall": recall_score(UpperCamelCase_			,	UpperCamelCase_    ),
												    "f1": fa_score(UpperCamelCase_			,	UpperCamelCase_    ),
												}

						# Data collator
						lowercase   =				DataCollatorWithPadding(UpperCamelCase_			,	pad_to_multiple_of=8    ) if training_args.fpaa else None

						# Initialize our Trainer
						lowercase   =				Trainer(
						    model=UpperCamelCase_			,	args=UpperCamelCase_			,	train_dataset=UpperCamelCase_			,	eval_dataset=UpperCamelCase_			,	compute_metrics=UpperCamelCase_			,	data_collator=UpperCamelCase_			,	)

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir    )

																		 # Evaluation
						lowercase   =				{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""    )

												lowercase   =				trainer.evaluate()

												lowercase   =				os.path.join(training_args.output_dir			,	"""eval_results.txt"""    )
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_			,	"""w"""    ) as writer:
																								logger.info("""***** Eval results *****"""    )
																								for key, value in result.items():
																														logger.info("""  %s = %s"""			,	UpperCamelCase_			,	UpperCamelCase_    )
																														writer.write("""%s = %s\n""" % (key, value)    )

																		results.update(UpperCamelCase_    )

																		 # Predict
						if training_args.do_predict:
												lowercase   =				TokenClassificationDataset(
												    token_classification_task=UpperCamelCase_			,	data_dir=data_args.data_dir			,	tokenizer=UpperCamelCase_			,	labels=UpperCamelCase_			,	model_type=config.model_type			,	max_seq_length=data_args.max_seq_length			,	overwrite_cache=data_args.overwrite_cache			,	mode=Split.test			,	)

												lowercase  , lowercase  , lowercase   =				trainer.predict(UpperCamelCase_    )
												lowercase  , lowercase   =				align_predictions(UpperCamelCase_			,	UpperCamelCase_    )

												lowercase   =				os.path.join(training_args.output_dir			,	"""test_results.txt"""    )
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_			,	"""w"""    ) as writer:
																								for key, value in metrics.items():
																														logger.info("""  %s = %s"""			,	UpperCamelCase_			,	UpperCamelCase_    )
																														writer.write("""%s = %s\n""" % (key, value)    )

																														   # Save predictions
												lowercase   =				os.path.join(training_args.output_dir			,	"""test_predictions.txt"""    )
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_			,	"""w"""    ) as writer:
																								with open(os.path.join(data_args.data_dir			,	"""test.txt"""    )			,	"""r"""    ) as f:
																														token_classification_task.write_predictions_to_file(UpperCamelCase_			,	UpperCamelCase_			,	UpperCamelCase_    )

						return results







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[Any]    ):
						main()


if __name__ == "__main__":
						main()



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
     import jax
@require_flax
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				FlaxAutoencoderKL







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      4
    UpperCamelCase			  =      3
    UpperCamelCase			  =      (32, 32)

    UpperCamelCase			  =      jax.random.PRNGKey(0				)
    UpperCamelCase			  =      jax.random.uniform(lowerCamelCase_   ,   ((batch_size, num_channels) + sizes)				)

    return {"sample": image, "prng_key": prng_key}







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      {
        """block_out_channels""": [32, 64],
        """in_channels""": 3,
        """out_channels""": 3,
        """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
        """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
        """latent_channels""": 4,
    }
    UpperCamelCase			  =      self.dummy_input
    return init_dict, inputs_dict
